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askeing/report-input-latency-obs.ipynb

Last active April 19, 2017 04:03
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2017-04-14 OBS (22 times) win10 90fps
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report-input-latency-obs.ipynb
{
"metadata": {
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import glob\n",
"import json \n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from os import environ\n",
"from IPython.display import display, HTML\n",
"\n",
"key_index = ['avg_time',\n",
" 'max_time', \n",
" 'med_time',\n",
" 'min_time',\n",
" 'std_dev']\n",
"\n",
"#\n",
"# Read report filename from environment varilable 'file' \n",
"#\n",
"filename = environ['file']\n",
"with open(filename) as data_file:\n",
" data = json.load(data_file)\n",
" d = pd.DataFrame(data) \n",
"\n",
"# drop empty 'time_list'\n",
"for c in d:\n",
" if (d[c]['time_list'] == []) :\n",
" d.drop(c, axis=1, inplace=True)\n",
" \n",
"# Select columns and rows\n",
"d.loc[key_index, :]"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>avg_time</th>\n",
" <td>23.6715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max_time</th>\n",
" <td>44.4444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>med_time</th>\n",
" <td>22.2222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min_time</th>\n",
" <td>11.1111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std_dev</th>\n",
" <td>9.05424</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 1,
"text": [
" test_chrome_gdoc_ail_type_10p_txt\n",
"avg_time 23.6715\n",
"max_time 44.4444\n",
"med_time 22.2222\n",
"min_time 11.1111\n",
"std_dev 9.05424"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Retrive 'run_time' from each run\n",
"runtime = pd.DataFrame([pd.DataFrame(d[c]['time_list'])['run_time'] for c in d]).T\n",
"runtime.columns = d.columns\n",
"runtime"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 2,
"text": [
" test_chrome_gdoc_ail_type_10p_txt\n",
"0 33.333333\n",
"1 44.444444\n",
"2 22.222222\n",
"3 22.222222\n",
"4 22.222222\n",
"5 22.222222\n",
"6 22.222222\n",
"7 11.111111\n",
"8 22.222222\n",
"9 22.222222\n",
"10 22.222222\n",
"11 33.333333\n",
"12 22.222222\n",
"13 33.333333\n",
"14 44.444444\n",
"15 22.222222\n",
"16 11.111111\n",
"17 11.111111\n",
"18 22.222222\n",
"19 11.111111\n",
"20 22.222222\n",
"21 22.222222\n",
"22 22.222222"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Report percentile\n",
"runtime.quantile([0.01, 0.95, 0.99], interpolation='nearest')"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.01</th>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.99</th>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 3,
"text": [
" test_chrome_gdoc_ail_type_10p_txt\n",
"0.01 11.111111\n",
"0.95 44.444444\n",
"0.99 44.444444"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Report summary\n",
"runtime.describe()"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>23.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>23.671498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>9.054237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 4,
"text": [
" test_chrome_gdoc_ail_type_10p_txt\n",
"count 23.000000\n",
"mean 23.671498\n",
"std 9.054237\n",
"min 11.111111\n",
"25% 22.222222\n",
"50% 22.222222\n",
"75% 22.222222\n",
"max 44.444444"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot input latency histogram\n",
"runtime.plot.hist(bins=20, \n",
" histtype='bar', \n",
" color=['green',], \n",
" subplots=True, layout=(1, 1), \n",
" figsize=(20, 10))\n",
"runtime.plot.box(subplots=True,\n",
" layout=(1, 1),\n",
" sharey=True, sharex=True, figsize=(20, 10))\n",
"plt.show()\n",
"plt.show()"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
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be1MzQALuB365ZG0z8GJVnQBsB27tvCrNg5V6D+Cuqjqt/TzbdVGa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\nywGSJEmSJEmSxvoOO2nhB/knqK0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10bcc1fd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10bcc1ed0>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": true,
"input": [],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [],
"prompt_number": 6
}
],
"metadata": {}
}
]
}
Raw
result-win10-obs-90fps.json
{
"test_chrome_gdoc_ail_type_10p_txt": {
"total_time": 544.444444444438,
"perceptual_speed_index": 0,
"time_list": [
{
"expected_frames": 0,
"psi": 0,
"frame_throughput": 0,
"long_frame": 0,
"si": 0,
"time_sequence": [],
"run_time": 33.33333333332848,
"freeze_frames": 0,
"folder": "test_chrome_gdoc_ail_type_10p_txt_1492142188",
"actual_paint_frames": 0
},
{
"expected_frames": 0,
"psi": 0,
"frame_throughput": 0,
"long_frame": 0,
"si": 0,
"time_sequence": [],
"run_time": 44.44444444444525,
"freeze_frames": 0,
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